The latest articles on DEV Community by Pranjal Kumar (@pranjal_kumar_858f7f08bf7). https://dev.to/pranjal_kumar_858f7f08bf7 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2868926%2F77046dbd-698e-4071-8751-5acb9dc5f963.png https://dev.to/pranjal_kumar_858f7f08bf7 en Pranjal Kumar Thu, 05 Nov 2026 18:34:39 +0000 https://dev.to/pranjal_kumar_858f7f08bf7/how-were-building-a-real-time-ai-interview-copilot-for-software-engineers-31ak https://dev.to/pranjal_kumar_858f7f08bf7/how-were-building-a-real-time-ai-interview-copilot-for-software-engineers-31ak <p>Building an AI application is one thing. Making it useful in a real-world situation, where timing and context matter, is another.</p> <p>That's one of the challenges we're working on while building Qevra AI.</p> <h3> The problem we're trying to solve </h3> <p>Technical interviews can be stressful, even for experienced software engineers. Sometimes candidates understand a concept but struggle to organise their thoughts and explain their approach clearly under pressure.</p> <p>We wanted to explore whether AI could provide useful support in these situations.</p> <h3> What we're building </h3> <p>Qevra AI is a real-time AI interview copilot for software engineers and professionals.</p> <p>The desktop application listens to interview questions and generates contextual answer suggestions. Candidates can provide their resume and job description so the AI can take their experience and the role into account.</p> <h3> The engineering challenges </h3> <p>Building this experience involves solving several interesting problems:</p> <ul> <li> <strong>Speech recognition:</strong> Converting spoken interview questions into usable text.</li> <li> <strong>Context management:</strong> Making sure suggestions consider the candidate's resume and target role.</li> <li> <strong>Response latency:</strong> Reducing the time between a question being asked and a useful suggestion appearing.</li> <li> <strong>Answer quality:</strong> Making responses sound natural and conversational rather than like textbook definitions.</li> </ul> <p>These challenges are interconnected. A fast response isn't useful if it misses the question, and a detailed response isn't useful if it arrives too late.</p> <h3> What we're learning </h3> <p>One important lesson so far is that building a useful AI product requires much more than choosing a language model. The surrounding experience, context, speech recognition and response quality matter just as much.</p> <p>We're continuing to improve Qevra AI and learn from real-world feedback.</p> <p>Website: <a href="https://qevraai.com/" rel="noopener noreferrer">https://qevraai.com/</a></p> <p>I'd love to hear from developers building AI products:</p> <p><strong>What has been your biggest challenge when building real-time AI applications — latency, context management, or output quality?</strong></p> ai webdev productivity startup